393 citations · 1.4k across the 36 of their papers we have counts for
38 papers
On the use of deep learning for phase recovery
Kaiqiang Wang, Li Song, Chutian Wang +8
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imag…
Mechanistic Modeling and Analysis of Thermal Radiation in Conventional, Microwave-assisted, and Hybrid Freeze Drying for Biopharmaceutical Manufacturing
Prakitr Srisuma, George Barbastathis, Richard D. Braatz
In freeze drying, thermal radiation has a significant effect on the drying process of vials located near the corner and edge of the trays, resulting in non-uniformity of the produc…
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
Iksung Kang, Yi Jiang, Mirko Holler +5
Three-dimensional inspection of nanostructures such as integrated circuits is important for security and reliability assurance. Two scanning operations are required: ptychographic…
The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage
Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun +8
: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma us…
Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time
Iksung Kang, Ziling Wu, Yi Jiang +5
Noninvasive X-ray imaging of nanoscale three-dimensional objects, e.g. integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational an…
Noise-resilient approach for deep tomographic imaging
Zhen Guo, Zhiguang Liu, Qihang Zhang +2
We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantage…
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
Fabian A. Braeu, Thanadet Chuangsuwanich, Tin A. Tun +4
: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH rob…
Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
Fabian A. Braeu, Alexandre H. Thiéry, Tin A. Tun +4
Purpose: The optic nerve head (ONH) undergoes complex and deep 3D morphological changes during the development and progression of glaucoma. Optical coherence tomography (OCT) is th…
Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE)
Qihang Zhang, Janaka C. Gamekkanda, Ajinkya Pandit +9
Extracting quantitative information about highly scattering surfaces from an imaging system is challenging because the phase of the scattered light undergoes multiple folds upon pr…
Physics-assisted Generative Adversarial Network for X-Ray Tomography
Zhen Guo, Jung Ki Song, George Barbastathis +5
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection…
Unified treatment of exact and approximate scalar electromagnetic wave scattering
Subeen Pang, George Barbastathis
Under conditions of strong scattering, a dilemma often arises regarding the best numerical method to use. Main competitors are the Born series, the Beam Propagation Method, and dir…
xSCYTE: Express Single-frame Cytometer through Tomographic Phase
Baoliang Ge, Yanping He, Mo Deng +13
Rapid, comprehensive, and accurate cell phenotyping without compromising viability, is crucial to many important biomedical applications, including stem-cell therapy, drug screenin…
Advantage of Machine Learning over Maximum Likelihood in Limited-Angle Low-Photon X-Ray Tomography
Zhen Guo, Jung Ki Song, George Barbastathis +5
Limited-angle X-ray tomography reconstruction is an ill-conditioned inverse problem in general. Especially when the projection angles are limited and the measurements are taken in…
Machine Learning Regularized Solution of the Lippmann-Schwinger Equation
Subeen Pang, George Barbastathis
Solution of the discretized Lippmann-Schwinger equation in the spatial frequency domain involves the inversion of a linear operator specified by the scattering potential. To regula…
A machine learning aided global diagnostic and comparative tool to assess effect of quarantine control in Covid-19 spread
Raj Dandekar, Chris Rackauckas, George Barbastathis
We have developed a globally applicable diagnostic Covid-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon p…
Limited-angle tomographic reconstruction of dense layered objects by dynamical machine learning
Iksung Kang, Alexandre Goy, George Barbastathis
Limited-angle tomography of strongly scattering quasi-transparent objects is a challenging, highly ill-posed problem with practical implications in medical and biological imaging,…
On the interplay between physical and content priors in deep learning for computational imaging
Mo Deng, Shuai Li, Iksung Kang +2
Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two im…
Neural Network aided quarantine control model estimation of global Covid-19 spread
Raj Dandekar, George Barbastathis
Since the first recording of what we now call Covid-19 infection in Wuhan, Hubei province, China on Dec 31, 2019, the disease has spread worldwide and met with a wide variety of so…
Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China
Raj Dandekar, George Barbastathis
In a move described as unprecedented in public health history, starting 24 January 2020, China imposed quarantine and isolation restrictions in Wuhan, a city of more than 10 millio…
Scalable additive manufacturing via integral image formation
Seok Kim, Jordan Jerome Handler, Young Tae Cho +2
Additive manufacturing techniques enable the fabrication of functional microstructures with mechanical and chemical properties tailored to their intended use. One common technique…
Learning to Synthesize: Robust Phase Retrieval at Low Photon counts
Mo Deng, Shuai Li, Alexandre Goy +2
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the…
Low Photon Budget Phase Retrieval with Perceptual Loss Trained Deep Neural Networks
Mo Deng, Alexandre Goy, Shuai Li +2
Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging proble…
High-Resolution Limited-Angle Phase Tomography of Dense Layered Objects Using Deep Neural Networks
Alexandre Goy, Girish Rughoobur, Shuai Li +3
We present a Machine Learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to 10. Whereas previous ap…
Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery
Mo Deng, Shuai Li, George Barbastathis
Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands. In this paper we propose…